Automated form generation system

ABSTRACT

Various embodiments, methods and systems for implementing a form generation system are provided. Generating forms includes dynamic generation, personalization, and optimization of the forms based on automation objects that instruct on how to construct, structure and present forms for personalized data capture experiences. In operation, a form generator engine receives a request from a computing device to access a form. The form generator engine accesses form generation automation rules that are based on form generation parameters and automation objects. Using form generation automation rules, form generation parameters are used to generate automation objects including an annotated schema, a machine learning model, and a layout. Based on the form generation automation rules the automation objects are used to generate the form such that at least a field or a section of the form is selected based on a relevance score associated with field or section. The form is communicated for display.

BACKGROUND

Organizations often capture user information to help provide their goodsor services to the user. Capturing user information can be based on datacapture experiences that provide electronic data capture forms orelectronic forms. Electronic data capture forms commonly are presentedvia online sites or other types of networked computing environments. Forexample, an organization may provide an electronic data capture formthat supports capturing user information, where the user enters theirinformation into fields of the electronic form.

SUMMARY

Embodiments of the present invention relate to methods, systems andcomputer storage media for automatically generating data capture forms(“forms”). In particular, generating forms includes dynamic generation,personalization, and optimization of forms based on form generationautomation objects (“automation objects”) that instruct on how toautomatically construct, structure and present forms for data captureexperiences. Forms are generated based on specific types of automationobjects that include an annotated schema, a machine learning model, andlayout templates. The form is automatically generated, personalized andoptimized using a form generation system.

In operation, at a high level, a form generator engine receives arequest from a computing device to access a form. The form generatorengine accesses form generation automation rules that are based on formgeneration parameters and automation objects for generating the forms.The form generation parameters indicate properties for generating theautomation objects and the form. Automation objects support schemaannotation, machine learning and layout templates for generating theform. The form generation parameters and automation objects aredynamically generated by or retrieved from components of the formgeneration system. Form generation parameters can include a set ofweights for fields or sections, a set of weighted parameters, devicetype data, business rules data, a raw schema, user activity data,analytics data, user submission data and user profile data, amongstother form generation parameters, that are used to execute the formgeneration automation rules. Automation objects can include an annotatedschema, a machine learning model, a layout repository of templates,associated with sections, and fields, amongst other elements, that areused to execute the form generation automation rules for generating theform. In particular, the form is generated based on the form generationautomation rules, such that at least a field or a section of the form isselected based on a relevance score associated with the field orsection. The form is communicated to cause display of the form on thecomputing device.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used in isolation as an aid in determining the scope of the claimedsubject matter

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of an exemplary form generation system inwhich embodiments described herein may be employed;

FIG. 2 a block diagram of an exemplary form generation system in whichembodiments described herein may be employed;

FIG. 3 is a flow diagram showing an exemplary method for implementing aform generation system, in accordance with embodiments described herein;

FIG. 4 is a flow diagram showing an exemplary method for implementing aform generation system, in accordance with embodiments described herein;

FIG. 5 is a flow diagram showing an exemplary method for implementing aform generation system, in accordance with embodiments described herein;and

FIG. 6 is a block diagram of an exemplary computing environment suitablefor use in implementing embodiments described herein.

DETAILED DESCRIPTION

Organizations providing goods or services to users often collectinformation from users. Capturing user information can be based on datacapture experiences that provide electronic data capture forms(“electronic forms” or “forms”). Collecting user information can beuseful for converting users into paying customers or gathering userinformation from users to provide services. Such data captureexperiences in some instances may require filling out forms havingseveral different sections, each section having different fields forcapturing different types of user information. For example, a userfilling out a form application, while applying for a credit card from abanking organization, may have to fill out a section on personalinformation and another section on financial history, so that thebanking organization can then process the credit card application. Theparticular information requested from the user can also vary based onthe user. For example, if the user is below a certain age, the user mayrequire a co-applicant on the credit card application and as suchadditional sections and fields are provided for receiving the userinformation.

By way of background, a data capture form (“form”) can refer to anelectronic form. A form can be associated with a data captureexperience. As part of a data capture experience, the form can begenerated based on design requirements that are characteristics andfeatures of the form that define how the form is constructed, structuredand presented as part of the data capture experience. Designrequirements can conventionally include a form schema, layouts, sectionsand fields of the form, and graphical control elements. A section of theelectronic form can include fields, where a section is a logicalgrouping of fields. For example, an address section can include street,city, state and zip code as fields. A fragment of the form can refer toone or more sections of the form. Fragments can be defined and reusedacross forms. For example, a “person fragment” can include both apersonal information section (e.g., first name, middle name, and lastname) in combination with an address section.

The form schema of the form can be formally or programmatically defined.For example, a form schema designer and/or data capture experiencedesigner constructs a layout, sections, fields in a formal language ordesign definition in order to capture or receive particular userinformation from a user entering the information into an electronicform. An application developer can access the design requirements todevelop specific types of data capture experiences in different forms.Form field values (“user input” or “user information”) can refer toinformation entered by a user into the form. The user can be a targetaudience for the form or a participant in a particular activityassociated with the form.

Organizations that use data capture experiences intend on making userconversions (i.e., achieve the desired intent for the data captureexperience) using the forms. For example, during a data captureexperience, a user is presented with a form, such that the user isconverted, when the user enters input that is captured via the form. Aconversion ratio is based on a number of users that filled the formcompared to a total number of users to whom the form was presented to.These organizations face several challenges when attempting to captureuser information from data capture experiences. Challenges can beexperienced based on the amount of user information to be captured,different types of computing devices, and frequent changes in the typeof user information to be captured.

By way of example, when there exists a significant amount of userinformation to be captured, forms and data capture experiences have tobe designed so that the user is not frustrated or hindered from enteringtheir information because they have a lot of information to be enteredinto the form. Several fields or sections of the form can at times beinapplicable to a user based on user input into other parts of the form.Also, when there are different varieties of computing devices havingdifferent capabilities for processing data capture experiences, the userexperience has to be at least consistent or convenient on the differentcomputing devices. Further, when the type of user information to becaptured changes frequently because of external influences or businesspriorities, alterations have to be made in a timely manner in order tocapture the proper user information.

Conventional technologies and tools are limited in providing solutionsto the above-described challenges. Conventionally, manual formdevelopment processes and personnel are used to design and createdifferent types of data capture experiences for forms. For example, anorganization can create a list of requirements for data captureexperiences, a form schema designer uses the requirements to design aform schema. A data capture experience designer then generates mocks ofthe look and feel of the data capture experience and specifically howthe form would look when presented. The data capture experience designercan additionally define the look of the form on specific computingdevices, such as a mobile phone or tablet.

Application developers and form authors receive input information (e.g.,form schema) from a form schema designer and translate form schemas intodata capture experiences that can be communicated to a user in anelectronic form. Application developers and form authors often implementmanually intensive workflows to fully develop data capture experiences.For example, an application developer can use an application developmentlanguage to develop the electronic forms based on the mocks and formschema. However, the application developer has to manually definedifferent types or versions of an electronic form, as needed to getdifferent types of user information. The application developer canoptionally develop different types of electronic forms for differenttypes of computing devices, or at times the application developer takesa “one form fits all approach.” In this regard, for different types ofform developers and authors, the forms can be built tediously to makethem adaptive; nonetheless they are still relatively static, in that,the electronic form is exactly the same for each user. Overall, themanual development process and the conventional static forms make forless than ideal data capture experiences for users, which can limitconversion of users. As such, a comprehensive system that addresses thechallenges identified above would improve the data capture experiencedevelopment for organizations and the user experience.

Embodiments of the present invention relate to methods, systems andcomputer storage media for automatically generating data capture forms(“forms”). In particular, generating forms includes dynamic generation,personalization, and optimization of forms based on form generationautomation objects (“automation objects”) that instruct on how toautomatically construct, structure and present forms and data captureexperiences. Forms are generated based on specific types of automationobjects that include an annotated schema, a machine learning model, andlayout templates. The form is automatically generated, personalized andoptimized using a form generation system.

The form generation system uses schema annotation, a machine learningmodel, and layout templates in a layout repository, in an integratedsystem, to automatically generate, combine and communicate forms. Forexample, a form can be generated and communicated to a client computingdevice of a user accessing the form and the client computing device candisplay or cause display of the form that provides a data captureexperience. Using form generation automation rules, that are based onform generation parameters and form generation automation objects, theform can be personalized, optimized and provided to be displayed.

In this regard, the form generation parameters refer to properties thatare defined, coded or scripted into a set of form generation automationrules that are applied for automatically generating forms. The formgeneration automation rules can include a first tier of form generationautomation rules and a second tier of form generation automation rules.In one exemplary embodiment, first tier form generation rules can simplybe offline processing protocols and second tier form generation rulescan be online processing protocols. The first tier of form generationautomation rules can be defined, coded or scripted as instructions forgenerating automation objects and the second tier of form generationautomation rules can be defined, coded or scripted as instructions forgenerating a form. The first tier and second tier of form generationautomation rules can be executed based on form generation parameters andautomation objects respectively. For example, a first tier of formgeneration automation rules can be associated with generating theannotated schema and the machine learning model as automation objects.In particular, generating the machine learning model can be based on thefirst tier of form generation automation rules defined using formgeneration parameters (e.g., user profile, previous submissions, andweighted parameters) such that the values are used in generating themachine learning model. The second tier of form generation automationrules can be associated with generating the form itself as an electronicform. Generating the form can be based on the second tier of formgeneration automation rules defined using automation objects (i.e.,annotated schema, machine learning model, and layout templates).Specific first tier and second tier form generation automation rules aredescribed in more detail below with reference to examples.

With reference to form generation parameters, form generation parametersinclude: a set of weights for fields or sections, a set of weightedparameters, device type data, business rules data, a raw schema, useractivity data, analytics data, user submission data and user profiledata, amongst other form generation parameters, that are used to executethe form generation automation rules. Form generation parameters can beused to generate automation objects can be generated offline (e.g.,machine learning model) or dynamically online, where the form generationparameters are identified, or accessed from storage. The form generationparameters can be associated with specific components in the formgeneration system that supports processing form generation parametersfor generating the automation objects and the forms.

In operation, at a high level, a form generator engine receives arequest from a computing device to access a form. The form generatorengine accesses form generation automation rules that are based on formgeneration parameters and automation objects for generating forms. Theform generation automation rules are programmatically defined as code,instructions, or scripts written for a special automated form generationrun-time environment to automate the execution of tasks that generatethe form. The form generation parameters indicate properties forgenerating the automation objects and the form. Automation objectssupport schema annotation, machine learning and layout templates forgeneration the form. The form generation parameters and automationobjects are dynamically generated by or retrieved from components of theform generation system. Form generation parameters are used to executethe form generation automation rules. Automation objects are used toexecute the form generation automation rules for generating the form. Inparticular, the form is generated based on the form generationautomation rules, such that at least a field or a section of the form isselected based on a relevance score associated with the field orsection. The form is communicated to cause display of the form on thecomputing device.

The form generation parameters can specifically be used to define,generate, and select automation objects (e.g., schema annotation,machine learning models, layout templates associated with graphicalcontrol elements, fragments, sections and fields). Some processing ofform generation parameters to generate automation objects can be doneoffline (e.g., generating the machine learning model). The components ofthe form generation system are associated with generating, providing oraccessing specific automation objects for the form. For example, anannotated schema generator generates an annotated schema based at leastin part on the raw schema and the set of weights of fields and sections.The weights of fields or sections, as quantified in the annotated schemarelevance score, can be based on a business value assessment. Forexample, the raw schema can contain a first name, a last name and amiddle name field and the business rules data may be used to annotatehigher weights to the first name and last name and a lower weight to amiddle name field. In another example, during an annotation process, afirst section in the raw schema can be weighted, based on business rulesdata, the first section is weighted higher than a second section basedon a quantified weight of the all the fields within the section. By wayof analogy, the raw schema provides the universe of defined sections andfields and the annotated schema generator proactively annotates sectionsand fields based on form generation parameters (e.g., weighted fields,business rules data, and user activity data and analytics data) and withadditional annotated data that provide instructions on how the annotatedschema can be used to generate the form. The annotated schema is thenused as input into the machine learning model generator. The raw schemacan include a first of set of fields or sections that are annotated intoa second set of fields or sections in the annotated schema. In oneexemplary implementation, the annotated schema is generated based onbusiness rules and updates to business rules. For example, a businessrule may require only a first name, last name and optional middleinitial; however an update to the business rule may require all three,as such a raw schema can be annotated depending on the applicablebusiness rule and can flexibly be updated from one rule to another. Inother words, upon updating the first set of business rules into a secondset of business rules (or updated set of business rules), the first setof fields or sections can be dynamically annotated into a newlyannotated second set of fields or sections in the annotated schema. Thenew second set of fields or sections of the annotated schema areassociated with the second set of business rules. In this regard, at afirst time, an annotated schema can have a set of fields or sectionsfrom the raw schema annotated using the first set of business rules, andat a second time, the annotated schema can have the set of fields orsections from the raw schema annotated using the second set of businessrules.

The annotated schema is generated to contain relevant fields or sections(e.g., a personal information section) having relevance scores(“annotated schema relevance score”) based on weights of fields. Therelevant score in the annotated schema indicate how relevant a field isto the business as the weights of fields are identified by businessrules data. The raw schema can include fields for user information to becaptured, such as personal information and address information and howthat information is programmatically represented. For example, anExtensible Markup Language (XML) can be used to define the raw schemaand then the annotated form schema. The raw schema can also beprogrammatically and dynamically annotated to represent business rulesdata, validation data and dependency data between data fields. Forexample, a “service eligibility” field can depend on “annual income” ora “rental car information” section can depend on the “age” of the userbecause of business rules data.

The raw schema can also be annotated into an annotated schema toassociate fields and sections with user information from previous useractivity data or analytics data that can be used to make additionaldecisions in generating the form. The annotations using the useractivity data and the analytics data can also be programmaticallyexecuted. In one exemplary implementation, an annotated schema isgenerated based on a first set of fields or sections in the raw schemathat are annotated into an annotated second set of fields or sections inthe annotated schema. An annotated schema supports defining an annotatedrelevance score for fields or sections in the annotated schema orselecting a layout template for forms. For example, the form generationsystem can skip serving a particular section when user informationalready exists for the fields or based on user activity data andanalytics data in the annotated schema, the form generation system canskip serving a section when a previous similarly situated user did notfill the section. In another example, the form generator engine systemmay automatically offer assistance to a user for a field or sectionwhere users have been known to have trouble with (e.g., validationerrors above a defined threshold).

The machine learning model generator generates a machine learning modelthat is used to support automatically generating the form. The machinelearning model uses algorithms that can learn to make predictions ondata in order to make data-driven predictions and decisions. The modelcan be built to make predictions on relevant fields or sections andprobable values of specific fields (i.e., auto-fill or auto-suggestionfunctionality) based on form generation parameters. In this regard, themachine learning model generator component also includes automationfunctions values (i.e., values for auto-fill function or auto-suggestfunction). Given data from all user submission data and weightedparameters, the machine learning model generator generates a model thatcan receive the annotated schema, user profile data (which can includedemographic data) and user submission data to provide a set of probablevalues for some of the fields and the relevance score of each field orsection.

Several different types of machine learning algorithms and techniques(data clustering, linear regression, polynomial regression, etc.) arecontemplated with embodiments of this invention. At a high level aselected machine learning algorithm operates to determine a relevancescore for fields of a form and determine default values (e.g., autofillor auto-suggest values) for fields. For example, a first user and asecond user may share similar demographic attributes such that, if thefirst user fills or skips a set of fields for a selected form, for thesecond user, based on the first user activity data, a relevance scorefor the set of fields can be determined and referenced when generatingthe selected form. The machine learning model generator applies thealgorithm to generate the machine learning model, the machine learningmodel is used to generate relevance scores (“machine learning relevancescore”) for fields and sections of the form and at least a subset of thesections or fields are selected for presentation based on the relevancescores. The relevance score in the model indicates how relevant thefield or section would be to the user. Advantageously, section relevancescores operate such that sections are prioritized for presentation basedon the sum of the relevance scores of fields within the section. Abusiness rule may further define a threshold relevance score forproviding a field or section to the user.

A machine learning model generator can operate to make predictions ofvalues based on user submission data and user profile data. Inoperation, the machine learning model generator accesses user profiledata, demographic data and user submission data (“dataset”). Forexample, location, gender, age group, income category, marital status,etc. In particular, user profile data include attributes that are uniqueto a particular individual and demographic data include attributes thatare common to a segment of users. User submission data can refer toprevious submission data that not only includes data submitted by a userbut also may include data submissions of a plurality of user that havefilled a particular form. It is contemplated that “previous submissiondata” “past submission data” and “past submission history” may be usedparadigmatically. The accessed dataset can be used to intelligentlypredict default values for fields. The scores for fields in a sectionare then used to calculate the relevance score for that particularsection. In one embodiment, the user profile data and the usersubmission data can be organized (e.g., stitched) for easy lookup in asingle database.

The dataset can be processed using different types of machine learningtools. In operation, the dataset can be transformed to facilitate dataclustering. For example, number data items can be transformed intoranges, and strings can be analyzed to determine their semantic meaning.Additionally, using natural language processing techniques, some datasetvalues can be replaced with new related values to provide the essentialmeaning of the values in the dataset, removal of unique attributespertaining to the end users etc. Other variations and combination oftransforms are contemplated with embodiments described herein. Asdiscussed in more detail below, the dataset and data clusteringtechniques can be used to support making predications on values based onthe dataset.

In an exemplary embodiment, one technique for deriving meaningfulinformation from data is data clustering, which can be executed on thedataset to generate scores for high dimensional data. In operation, thetransformed dataset is received as input to execute a clusteringalgorithm. The clustering algorithm operates to segment the usersaccording to the user profile data and data attributes (i.e., fields ofthe form). Dimensions of data associated with the transformed datasetare determined or reduced and the dataset is clustered. The clustereddataset is utilized in making predictions for values of fields insections of the form, advantageously at the time when the form isgenerated. The parameters for each new user can be used as input toprocess against the cluster dataset. The parameters and cluster datasetare used to predict values based on determining the proximity of theuser to a cluster and deriving values to the proximate cluster. Theparameters can include user profile data, user demographic data, andform fields values the user has entered in previous fields or sectionsof the form during the session.

By way of example, when a form request is received from a user, theknown attributes of the user profile data and demographic data can beused to identify a cluster which matches the given user. Uponidentifying the cluster, values from the cluster can be used aspredictors or default values to fill the form associated with the user.Several different strategies can be adopted to extract values from anidentified cluster of the cluster dataset. For example, a mean value ora mode value can be determined for certain types of values in theidentified cluster and the calculated mean or mode can be used as adefault value.

The machine learning model generator further supports generatingrelevance scores. At a high level, the form generation system operatesto provide the most relevant fields to the user in the form that isgenerated and provided for display. Presenting the most relevant fieldsin part includes hiding irrelevant fields and sections and presentingfields in a customized order. As such, a relevance score is associatedwith each field. The relevance score (“machine learning relevancescore”) can be a mathematical function based on a confidence score, abusiness importance score (e.g., annotated schema score) and adependency score of a field. The mathematical function is shown below:

Score of field x _(i)=f(c(confidence),b(business_relevance),d(dependence_rank))=Kc+Mb+Nd

In function above, K, M, N are constants. Confidence of the fieldx_(i)=(Total number of fields with the predicted value/Total number ofrows of data). Business_relevance of the field x_(i)=A*Number of rulesin field+f(mandatory) where A=constant, f(mandatory)=1 if x_(i) ismandatory else 0.1. Dependence_rank=B*Σbusiness_relevance*f(mandatory)where Σ is the summation of all dependent fields of x_(i), whereB=constant.

For every data attribute (e.g., column data) the machine learning modelgenerator can output the default value of the field and its associatedrelevance score. It is contemplated that different strategies, such asaggregation of such scores across sections, can decide the relevance ofsections. In addition, depending on whether the form field value is aunique attribute or a value that should be predicted by the model, theform is generated based the specific type of field. Other machinelearning models (e.g., logistic regression, multinomial regression,linear regression) can be used to predict values and providecorresponding scores for fields as discussed above.

The layout repository stores and provides different layout templates. Alayout includes multiple widgets, graphical controls and layout orderingfeatures that support providing a data capture experience. The layout ofthe data capture experience can be dynamically generated or identifiedbased on form generation parameters and user information (e.g., currentsession user inputs) to provide the user with a tailored experience. Thelayout repository can be updated with information based on analyticsdata. For example, layouts that have been associated with moreconversions might be associated with a higher relevance score (i.e.,layout relevance score) such that the particular layout has a higherlikelihood to be selected compared to other layouts that have lowerrelevance score. In this regard, a layout for a section that led to aconversion would increase the likelihood that the same layout would beselected for a subsequent section. In addition, fields in a layout canbe associated with properties that are referenced and used whengenerating the field in the form. Field properties can be based onanalytics data, user activity data and other identified data tracked forthe particular field. Actions of previous users who have used a field ina selected form can be used to define a property for the field. Forexample, based on analytics data, a layout can have a field defined witha property that indicates “always show help” because of a high errorrate identified from other users when filling the field or an averagetime taken to fill the field. Other variations and combination ofproperties for fields are contemplated with embodiments describedherein.

The layout repository can be part of the broader capacity for the formgenerator engine system to support device-based optimization, where theform is generated based in part on the specific computing device theuser is operating. For example, a user on a mobile phone, tablet, laptopor desktop can have their layout or graphical control elementsdynamically generated based on their computing device. In anotherembodiment, the form can also be presented in selectable sections orfragments for improved presentation and interaction by the user. By wayof example, the selected layout template can be based on a determinedbest fit number of fields to be presented to the user based on thedevice display characteristics. Further, graphical control elements(e.g., radio button, dropdown, scroll, etc.) associated with the layouttemplates can also be selected based on the user's computing device.

The form generation system operates to access the form generationautomation rules (e.g., the second tier of form generation automationrules) and use the automation objects to generate the form. Inparticular, the form generator engine can access embedded rules (e.g.,annotated form schema) or explicit rules (e.g., rules engine) that arescripted or coded as instructions to generate the form. For example,using processing the annotated form schema through the machine learningmodel, relevant sections above a relevance threshold can be selected togenerate the form. Also, automated function values, identified based onthe machine learning model, are presented as part of the form.

The form generation system can also further dynamically generate theform based on personalization and optimization, via one or more formgeneration iterations, based on current session user information. Thecurrent session user information can be used to determine how to presentsections of the form to the user. For example, selecting a layouttemplate can be based, at least in part, on current session userinformation associated with the fields, or the relevance score for asection can be refined based on user activity or analytics. Based onuser information received during a current session, form generationactions can be taken to dynamically update how the form is presented.The form is generated based on the form generation parameters andautomation objects and communicated to the computing device. The user ofthe computing device can access a personalized, optimized andautomatically generated form.

Advantageously, this invention alleviates the hardship associated withcomplex development cycles for data capture experiences with automated,dynamic, personalized and optimized form generation system. Inparticular, the form generation system leverages machine learning togain additional insights into potential user information, intent andactions. For example, a basic credit card request form, based onembodiments of the present invention, can be generated based ondeductions from the machine learning model. With the machine learningmodel, if the user is from an urban area and within the 35-55 age range,the user can be automatically presented with additional options toupgrade the credit card form to a premium credit card request form.Another user, who does not meet the same requirements, would notautomatically have the basic credit card request form updated to thepremium credit card request form.

The form generation system uses a set of unconventional rules (i.e.,sets of form generation automation rules) to automate the generation offorms. In particular, the set of rules are associated with formgeneration parameters (i.e., first tier rules) and automation objects(i.e., second tier rules). The selected defined set of rules preventsbroad preemption of all rules for automated form generation. The formgeneration system further provides for a specific improvement to theoperation of the conventional form generation systems and for generationtechnology, in that, electronic forms can be dynamically generatedinstead of relying on conventional technology static forms. For example,only the most relevant portions of forms may be computed, identified,and communicated to a user, as such limiting the computing resourcesbased on dynamic portions of the forms compared to overly broadcomputing processing of irrelevant portions of the form that are notapplicable to the user. Further, the forms can be personalized andoptimized based on user activity data and analytics data using theautomation objects and the form generation parameters, as describedabove.

Moreover, the operations for generating dynamic generation,personalization and optimization of forms is not conventional in thetechnology field and the form generation system addresses atechnological problem of manual development of forms by implementing asolution specific to that technological environment. The solution isalso different from the manner suggested by routine or conventional use(i.e., static forms) within the field. As such, this invention presentsan improvement to the computing operations of data capture generationand an improvement to the overall technology field of generating forms.

With reference to FIG. 1, FIG. 1 illustrates an exemplary formgeneration system 100 in which implementations of the present disclosuremay be employed. In particular, FIG. 1 shows a high level architectureof form generation system 100 having components in accordance withimplementations of the present disclosure. It should be understood thatthis and other arrangements described herein are set forth only asexamples. In addition, a system, as used herein, refers to any device,process, or service or combination thereof. A system may be implementedusing components or generators as hardware, software, firmware, aspecial-purpose device, or any combination thereof. A system may beintegrated into a single device or it may be distributed over multipledevices. The various components or generators of a system may beco-located or distributed. For example, although discussed for clarityas the content application component, operations discussed can beperformed in a distributed manner. The system may be formed from othersystems and components thereof. It should be understood that this andother arrangements described herein are set forth only as examples.

Among other components or generators not shown, form generation system100 includes a computing device 110 having a client form generatorcomponent 120. The form generation system 100 also includes formgenerator engine 130 including a plurality of form generator engineprocess components (130A, 130B, 130C and 130D). The components of theform generation system 100 may communicate with each other over one ormore networks (e.g., public network or virtual private network “VPN”).The network (not shown) may include, without limitation, one or morelocal area networks (LANs) and/or wide area networks (WANs). Thecomputing device 110 can be a client computing device that correspondsto the computing device described herein with reference to FIG. 6.

The components of the form generation system 100 can operate together toprovide functionality for automated, dynamic, personalized and optimizedform generation described herein. The form generation system 100supports forms on the computing device 110. In particular, the computingdevice 110 includes a client form generator component 120 that operatesto process data used for providing data capture experiences. The clientform generator component 120 can be part of different types of computingdevices (e.g., computing devices 110A, 110B and 110C) each havingdifferent capabilities for accessing forms. The client form generatorcomponent 120 can receive a request (e.g., request 112A) for a form. Forexample, a user on the computing device 110 may navigate to a browserapplication and select a link that operates to communicate a request fora form. The request for the form may automatically include additionalinformation (e.g., different types of form generation input, such as,device type data including device capabilities, associated withgenerating the form.

The client form generator component 120 can also operate to receive theform from the form generator engine 130. In particular, the form can bereceived (e.g., form data 114A) and processed at the client formgenerator component 120. The form can be received based on operationsperformed at the form generator engine 130, as discussed herein. Theform can be received in whole or partially (e.g., ‘form fragments) suchthat a user at the computing device 110 can provide input based on thereceived portion of the form. The client form generator component 120also operates to cause display of the form at a display of the computingdevice 110.

It is contemplated that user information 112B (e.g., current sessionuser information) can be communicated from the client form generatorcomponent 120. The user session data can refer to data received as userinput after fields, sections, or fragments of a form are communicated tothe client form generator component 120. User information can alsooperate as form generator input data used in dynamically makingadditional decisions on how to generate the form. As described herein inmore detail, specifically current session user information can beiteratively processed in a data capture experience to generatesuccessive form data (e.g., a second portion of the form data 114B) ofthe form.

The form generator engine 130 is responsible for generating forms. Theform generator engine 130 operates with several form generator engineprocess components (e.g., form generator engine process components 130A,130B, 130C, and 130D) to automatically and dynamically generatespersonalized and optimized forms. Form generator engine processcomponents operate to access, identify and generate different types ofform generation parameters that are used to make decisions on how togenerate the form. At a high level, the form can be generated andcommunicated based on a request (e.g. request 112A) received from theclient form generator component 120 via the computing device 110. Theaccesses the plurality of form generator engine process components(e.g., form generation automation rules, form generation parameters, andautomation objects) and performs operations that support generatingforms.

The form generator engine 130 communicates the form (e.g., form data114A) to the computing device 100, where it is caused to be displayed sothat a user enters user information. The form generator engine 130 canalso receive user information 112B (e.g., current session userinformation) from the client form generator component 120 to dynamicallymake additional decisions on how to generate the form. The formgenerator engine 130 supports iterative reception of user information tosupport communicating successive form data (e.g., a second portion ofthe form data 114B) of the form.

Embodiments of the present invention can further be described withreference to FIG. 2, where FIG. 2 further illustrates components in theform generator engine 130. The form generator engine 130 furtherincludes the form generator component 140 having a form generationautomation rules 142, an annotated schema generator 150, a machinelearning model generator 160, a layout repository component 170, userprofile data 160, a plurality of additional form generator engineprocess components (i.e., weighted parameters 190A, user submission data190B, weighted fields or sections data 190C, business rules data 190D,raw schema 190E, user activity data 190F, and analytic data 190G.) Theform generator component 140 is configured to perform different types ofoperations to provide form generation system functionality describedherein. The form generator component 140 uses schema annotation, machinelearning models, and layout templates, in an integrated system, toautomatically generate, combine and communicate forms. In this regard,the automated schema generator 150, the machine learning model generator160 and the layout repository 170 support the functionality herein byproviding schema annotation, machine learning models and layouttemplates.

The components are associated with generating, providing or accessingspecific automation objects for the form. For example, an annotatedschema generator 150 generates an annotated schema that includes fieldsor sections having annotated schema relevance scores. The annotatedschema relevance score is based on corresponding weights of the fieldsor the sections, the annotated schema relevance score indicates abusiness value assessment based on business rules data. The annotatedschema generator 150 generates the annotated schema based on a rawschema. In operation, a first set of fields or sections in the rawschema is annotated into a second set of fields or sections in theannotated schema using business rules data associated with a set ofweights of the fields and sections, or validation data, or dependencydata. The annotated schema generator 150 can also generate the annotatedschema based on the raw schema, where the raw schema is annotated usinguser activity data and analytics data. The user activity data andanalytics data are programmatically associated with the fields orsections. Annotating a first set of fields or sections in the raw schemainto a second set of fields or sections supports defining an annotatedrelevance score for fields or sections in the annotated schema orselecting a layout template for forms.

The machine learning model generator 160 generates a machine learningmodel that is used to support automatically generating the form. Themachine learning model generator 160 access the annotated schema fromthe annotated schema generator 150. The machine learning model generator160 processes the annotated schema having fields or sections associatedwith weights (i.e., an annotated schema relevance score) and updates thefields and sections of the annotated schema with a machine learningrelevance score. The machine learning relevance score indicates aquantified relevance of the fields and sections to an identified userand operates as a relevance score for generating and displaying fieldsand sections as part of forms. The machine learning model generator 160also generates a machine learning model that supports identifyingautomated function values. An automated function value is a probablevalue for fields or sections of the form, the probable value isgenerated based on an annotated schema, user profile data and usersubmission data.

The layout repository component 170 and provides different layouttemplates. A layout includes multiple widgets, graphical controls andlayout ordering features that support providing a data captureexperience. The layout repository component 170 provides a plurality oflayout templates, where the layout of the data capture experience can bedynamically generated or identified based on form generation parametersand user information (i.e., current session user inputs) to provide theuser with a tailored experience. A selected layout template that isretrieved from the layout repository component 170 can be dynamicallydetermined based on an annotated schema and the device type data. Adisplay order in the layout template for fields or sections of the datacapture is based on the corresponding relevance score. The layoutrepository component 170 can also receive updates from analytics data,such that the plurality of layout templates are associated with a layoutrelevance score corresponding to their conversion rate of users. Layouttemplates having high layout relevance score have a higher likelihood tobe selected compared to other layouts templates that have lower layoutrelevance scores.

The form generator component 140 operates to access the form generationautomation rules 142 and use the automation objects to generate theform. In particular, the form generator component 140 can accessembedded rules (e.g., annotated form schema) or explicit rules (e.g.,rules engine) that are scripted or coded to generate the form. Forexample, using processing the annotated form schema through the machinelearning model, relevant sections above a relevance threshold can beselected to generate the form. Also, automated function values,identified based on the machine learning model, are presented as part ofthe form. The form generator component 140 can also further dynamicallygenerates the form based on personalization and optimization, via one ormore form generation iterations, based on current session userinformation. The current session user information can be used todetermine how to present sections of the form to the user. For example,selecting a layout can be based, at least in part, on current sessionuser information associated with the fields, or the relevance score fora section can be refined based on user activity or analytics. Based onuser information received during a current session, form generationactions can be taken to dynamically update how the form is presented.The form is generated based on the form generation parameters andautomation objects and communicated to the computing device. The user ofthe computing device can access a personalized, optimized andautomatically generated form.

With reference to FIGS. 3, 4 and 5, flow diagrams are providedillustrating methods for implementing a form generation system. Themethods can be performed using the form generation system describedherein. In embodiments, one or more computer storage media havingcomputer-executable instructions embodied thereon that, when executed,by one or more processors, can cause the one or more processors toperform the methods in the form generation system.

Turning to FIG. 3, a flow diagram is provided that illustrates a method300 for implementing a form generation system. Initially at block 310, arequest for a form is received. The request can be received with otherform generation parameters (e.g., device type data). At block 320, formgeneration automation rules are accessed. The form generation automationrules are defined based on form generation parameters and formgeneration automation objects. Based on form generation automationrules, the form is generated using one or more automation objects. Theone or more automation objects are utilized or accessed when generatinga field or a section of the form based on a relevance score associatedwith the field or section. At block 340, the form is communicated tocause display of the form.

Turning to FIG. 4, a flow diagram is provided that illustrates a method400 for implementing a form generation system. Initially at step 410, afirst portion of a form is received. The first portion of the form isreceived in response to a request having one or more form generationparameters. The one or more form generation parameters comprises adevice type data to receive the form, wherein based on the annotatedschema and the device type data, a layout template can be dynamicallydetermined for the first portion of the form.

The first portion of the form is generated using one or more automationobjects and based on form generation automation rules defined based onform generation parameters and automation objects. The one or moreautomation objects include instructions to generate a field or sectionof the form associated with the field or section. At block 420, thefirst portion of the form is caused to be displayed. Causing display ofthe first portion of the form is based on the layout template that isdynamically selected can be based on a display order, in the layouttemplate, for fields or sections of the form. The display order is basedon a machine learning relevance score that indicates a quantifiedrelevance of fields and sections of a form to an identified user. Themachine learning relevance score operates as a relevance score forgenerating and displaying fields and sections as part of forms. Thefirst portion can specifically have a probable value for fields orsections of the form, the probable value is generated based on anannotated schema, user profile data and user submission data.

At block 430, a first set of form field values is received based on thefirst portion of the form. At block 440, the first set of form fieldvalues is communicated to cause generation of a second portion of theform. At block 450, the second portion of the form is received. Thesecond portion of the form is generated based at least in part on thefirst set form field values, a machine learning model and the formgeneration automation rules. At block 460, the second portion of theform is caused to be displayed. Causing display of the form (e.g., thefirst portion or the second portion) can also include causing display ofone or more automated function values. An automated function value is aprobable value for fields or sections of the form, the probable value isgenerated based on a machine learning model using an annotated schem,user profile data, and user submission data.

Turning to FIG. 5, a flow diagram is provided that illustrates a method500 for implementing a form generation system. Initially at step 510, afirst tier of form generation automation rules is accessed. The firsttier of form generation automation rules are defined based on formgeneration parameters. At block 520, one or more automation objects aregenerated based on the first tier of form generation automation rules.At block 530, a request for a form is received. At block 540, a secondtier of form generation automation rules is accessed. The second tier ofform generation automation rules are defined based on automationobjects. At block 550, the form is generated, based on the second tierof form generation automation rules, using the one or more automationobjects. The one or more automation objects comprising an annotatedschema having instructions to generate a field or section of the forminstructions to generate a layout of the form based on a selected layouttemplate. At block 560, the form is communicated to cause display of theform.

With reference to the form generation system 100, embodiments describedherein support dynamic generation, personalization, and optimization ofthe form. The form generation system components refer to integratedcomponents that implement the image search system. The integratedcomponents refer to the hardware architecture and software frameworkthat support functionality using the form generation system components.The hardware architecture refers to physical components andinterrelationships thereof and the software framework refers to softwareproviding functionality that can be implemented with hardware operatedon a device. The end-to-end software-based form generation system canoperate within the other components to operate computer hardware toprovide form generation system functionality. As such, the formgeneration system components can manage resources and provide servicesfor the form generation system functionality. Any other variations andcombinations thereof are contemplated with embodiments of the presentinvention.

By way of example, the form generation system can include an API librarythat includes specifications for routines, data structures, objectclasses, and variables may support the interaction the hardwarearchitecture of the device and the software framework of the formgeneration system. These APIs include configuration specifications forthe form generation system such that the components therein cancommunicate with each other for form generation, as described herein.

Having identified various component of the form generation system 100,it is noted that any number of components may be employed to achieve thedesired functionality within the scope of the present disclosure.Although the various components of FIG. 1 are shown with lines for thesake of clarity, in reality, delineating various components is not soclear, and metaphorically, the lines may more accurately be grey orfuzzy. Further, although some components of FIG. 1 are depicted assingle components, the depictions are exemplary in nature and in numberand are not to be construed as limiting for all implementations of thepresent disclosure. The form generation system 100 functionality can befurther described based on the functionality and features of theabove-listed components.

Other arrangements and elements (e.g., machines, interfaces, functions,orders, and groupings of functions, etc.) can be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed by oneor more entities may be carried out by hardware, firmware, and/orsoftware. For instance, various functions may be carried out by aprocessor executing instructions stored in memory.

Having briefly described an overview of embodiments of the presentinvention, an exemplary operating environment in which embodiments ofthe present invention may be implemented is described below in order toprovide a general context for various aspects of the present invention.Referring initially to FIG. 6 in particular, an exemplary operatingenvironment for implementing embodiments of the present invention isshown and designated generally as computing device 600. Computing device600 is but one example of a suitable computing environment and is notintended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing device 600be interpreted as having any dependency or requirement relating to anyone or combination of components illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc. refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The invention may alsobe practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With reference to FIG. 6, computing device 600 includes a bus 610 thatdirectly or indirectly couples the following devices: memory 612, one ormore processors 614, one or more presentation components 616,input/output ports 618, input/output components 620, and an illustrativepower supply 622. Bus 610 represents what may be one or more busses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 6 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Also, processors have memory. Werecognize that such is the nature of the art, and reiterate that thediagram of FIG. 6 is merely illustrative of an exemplary computingdevice that can be used in connection with one or more embodiments ofthe present invention. Distinction is not made between such categoriesas “workstation,” “server,” “laptop,” “hand-held device,” etc., as allare contemplated within the scope of FIG. 6 and reference to “computingdevice.”

Computing device 600 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 600 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable media may comprise computerstorage media and communication media.

Computer storage media include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by computingdevice 600. Computer storage media excludes signals per se.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

Memory 612 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 600includes one or more processors that read data from various entitiessuch as memory 612 or I/O components 620. Presentation component(s) 616present data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, etc.

I/O ports 618 allow computing device 600 to be logically coupled toother devices including I/O components 620, some of which may be builtin. Illustrative components include a microphone, joystick, game pad,satellite dish, scanner, printer, wireless device, etc.

Embodiments described in the paragraphs above may be combined with oneor more of the specifically described alternatives. In particular, anembodiment that is claimed may contain a reference, in the alternative,to more than one other embodiment. The embodiment that is claimed mayspecify a further limitation of the subject matter claimed.

The subject matter of embodiments of the invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

For purposes of this disclosure, the word “including” has the same broadmeaning as the word “comprising,” and the word “accessing” comprises“receiving,” “referencing,” or “retrieving.” Further the word“communicating” has the same broad meaning as the word “receiving,” or“transmitting” facilitated by software or hardware-based buses,receivers, or transmitters” using communication media described herein.Also, the word “initiating” has the same broad meaning as the word“executing or “instructing” where the corresponding action can beperformed to completion or interrupted based on an occurrence of anotheraction. In addition, words such as “a” and “an,” unless otherwiseindicated to the contrary, include the plural as well as the singular.Thus, for example, the constraint of “a feature” is satisfied where oneor more features are present. Also, the term “or” includes theconjunctive, the disjunctive, and both (a or b thus includes either a orb, as well as a and b).

For purposes of a detailed discussion above, embodiments of the presentinvention are described with reference to a distributed computingenvironment; however the distributed computing environment depictedherein is merely exemplary. Components can be configured for performingnovel aspects of embodiments, where the term “configured for” can referto “programmed to” perform particular tasks or implement particularabstract data types using code. Further, while embodiments of thepresent invention may generally refer to the distributed data objectmanagement system and the schematics described herein, it is understoodthat the techniques described may be extended to other implementationcontexts.

Embodiments of the present invention have been described in relation toparticular embodiments which are intended in all respects to beillustrative rather than restrictive. Alternative embodiments willbecome apparent to those of ordinary skill in the art to which thepresent invention pertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects hereinabove set forthtogether with other advantages which are obvious and which are inherentto the structure.

It will be understood that certain features and sub-combinations are ofutility and may be employed without reference to other features orsub-combinations. This is contemplated by and is within the scope of theclaims.

The invention claimed is:
 1. A computer-implemented for generating datacapture forms, the method comprising: receiving a request for a form;accessing form generation automation rules, the form generationautomation rules are defined based on form generation parameters andautomation objects; based on the form generation automation rules,generating the form using one or more automation objects, wherein theone or more automation objects are utilized to generate a field orsection of the form based on a relevance score associated with the fieldor section; and communicating the form to cause display of the form. 2.The method of claim 1, wherein the one or more automation objectsinclude an annotated schema, wherein the annotated schema includesfields or sections having an annotated schema relevance score based oncorresponding weights of the fields or the sections, wherein theannotated schema relevance score indicates a business value assessmentbased on business rules data.
 3. The method of claim 1, wherein the oneor more automation objects include an annotated schema, wherein theannotated schema is generated based on a raw schema, wherein a first setof fields or sections in the raw schema is annotated into a second setof fields or sections in the annotated schema using one or more of thefollowing: business rules data associated with a set of weights of thefields and sections, validation data, and dependency data.
 4. The methodof claim 1, wherein the one or more automation objects include anannotated schema, wherein the annotated schema is generated based on araw schema, wherein the raw schema is annotated using user activity dataand analytics data that are programmatically associated with the fieldsor sections, wherein annotating a first set of fields or sections in theraw schema into a second set of fields or sections supports defining anannotated relevance score for fields or sections in the annotated schemaor selecting a layout template for forms.
 5. The method of claim 1,wherein the one or more automation objects comprise a machine learningmodel, wherein the machine learning model supports identifying one ormore automated function values, wherein an automated function value is aprobable value for fields or sections of the form, wherein the probablevalue is generated based on an annotated schema, user profile data anduser submission data.
 6. The method of claim 1, wherein one or moreautomation objects comprise a machine learning model, wherein themachine learning model processes an annotated schema having fields orsections and updates the fields and sections of the annotated schemawith a machine learning relevance score, the machine learning relevancescore indicates a quantified relevance of the fields and sections to anidentified user and operates as a relevance score for generating anddisplaying fields and sections as part of forms.
 7. The method of claim1, further comprising: accessing device type data associated with therequest; and dynamically determining a layout template from a pluralityof layout templates based on the device type data, wherein causingdisplay of the form is based on the layout template that is dynamicallyselected for the form, wherein a display order in the layout templatefor fields or sections of the data capture is based on correspondingrelevance scores of the fields or sections.
 8. One or more computerstorage media having computer-executable instructions embodied thereonthat, when executed, by one or more processors, cause the one or moreprocessors to perform a method for automatically generating data captureforms, the method comprising: receiving a first portion of a form,wherein the first portion of the form is generated using one or moreautomation objects and based on form generation automation rules definedbased on form generation parameters and automation objects; wherein theone or more automation objects are utilized to generate a field orsection of the first portion of the form, and causing display of thefirst portion of the form associated with the field or the section. 9.The media of claim 8, wherein a first tier of form generation automationrules are defined based on form generation parameters for generatingautomation objects, and wherein a second tier of form generationautomation rules are defined based on automation objects for generatingforms.
 10. The media of claim 8, wherein the first portion of the formis received in response to a request having one or more form generationparameters, the one or more form generation parameters comprising adevice type data, wherein based on the annotated schema and the devicetype data, a layout template is dynamically determined for the firstportion of the form.
 11. The media of claim 8, wherein causing displayof the first portion of the form is based on a layout template that isdynamically selected for the form, wherein a display order in the layouttemplate for fields or sections of the data capture is based on amachine learning relevance score that indicates a quantified relevanceof fields and sections of forms to an identified user or segment ofusers and operates as a relevance score for generating and displayingfields and sections as part of forms.
 12. The media of claim 8, whereincausing display of the first portion of the form further comprisescausing display of one or more automated function values, wherein anautomated function value is a probable value for fields or sections ofthe form, wherein the probable value is generated based on a machinelearning model using an annotated schema, user profile data and usersubmission data.
 13. The media of claim 1, the method furthercomprising: receiving a first set of form field values based on thefirst portion of the form; communicating the first set of form fieldvalues to cause generation of a second portion of the form; receivingthe second portion the form, wherein the second portion of the form isgenerated based at least in part on the first set of form field values,a machine learning model and the form generation automation rules; andcausing display of at least the second portion of the form.
 14. A formgeneration system for generating data capture forms, the systemcomprising: a means for accessing a first tier of form generationautomation rules, the first tier of form generation automation rules isdefined based on form generation parameters; a means for generating oneor more automation objects based on the first tier of form generationautomation rules and form generation parameters; a means for receiving arequest for a form; a means for accessing a second tier of formgeneration automation rules, the form generation automation rules aredefined based on automation object; a means for generating the formusing the one or more automation objects, wherein the one or moreautomation objects comprising an annotated schema having instructions togenerate a field or section of the form; and a means for communicatingthe form to cause display of the form.
 15. The system of claim 14,wherein based on the form generation parameters, the form generationparameters comprise one or more of the following: a set of weights forfields or sections, a set of weighted parameters, device type data,business rules data, a raw schema, user activity data, analytics data,user submission data and user profile data; and wherein, the one or moreautomation objects comprise one or more of the following: the annotatedschema, a machine learning model and a plurality of layout templates.16. The system of claim 14, wherein the annotated schema includes fieldsor sections having an annotated schema relevance score based oncorresponding weights of the fields or the sections, wherein theannotated schema relevance score indicates a business value assessmentbased on business rules data.
 17. The system of claim 14, wherein theannotated schema is generated based on a raw schema, wherein the rawschema is annotated using user activity data and analytics data that areprogrammatically associated with the fields or sections, whereinannotating a first set of fields or sections in the raw schema into asecond set of fields or sections supports defining an annotatedrelevance score for fields or sections in the annotated schema orselecting a layout template for forms.
 18. The system of claim 14,further comprising: a means for dynamically determining a layouttemplate from a plurality of layout templates based on the annotatedschema or a device type data; a means for causing display of the formbased on the layout template, wherein a display order for fields orsections of the form in the layout template is based on a machinelearning relevance score that indicates a quantified relevance of thefields and sections to an identified user; and a means for updating thelayout template based on analytics data, wherein a layout relevancescore, based on analytics data that tracks conversions associated withlayout templates, indicates a likelihood of the layout template to beselected.
 19. The system of claim 14, further comprising: a means fordynamically determining, based on user activity data and analytics data,one or more properties of a field in the form or a layout template froma plurality of layout templates; a means for receiving the formassociated with the layout template; a means for causing display of theform, based on the layout template and a corresponding display order ofa field or section, wherein display order is based on a relevance scoreof the field or section; and a means for causing display of one or moreautomated function values, wherein an automated function value is aprobable value for fields or sections of the form, wherein the probablevalue is generated based on a machine learning model using an annotatedschema, user profile data, and user submission data.
 20. The system ofclaim 14, further comprising: a means for receiving a first set of userinputs based on a first portion of the form; a means for communicatingthe first set of user inputs to cause generation of a second portion ofthe form; a means for receiving the second portion the form, wherein thesecond portion of the form is generated based at least in part on thefirst set of user inputs and form generation automation rules; and ameans for causing display of at least the second portion of the form.